所以我正在尝试创建一个自编码器，它可以处理文本评论并找到一个低维度的表示。我使用的是Keras，并且希望我的损失函数能够将自编码器的输出与嵌入层的输出进行比较。不幸的是，它给我带来了以下错误。我相当确定问题出在我的损失函数上，但我似乎无法解决这个问题。
自编码器
print X_train.shapeinput_i = Input(shape=(200,))embedding = Embedding(input_dim=weights.shape[0],output_dim=weights.shape[1], weights=[weights])(input_i)encoded_h1 = Dense(64, activation='tanh')(embedding)encoded_h2 = Dense(32, activation='tanh')(encoded_h1)encoded_h3 = Dense(16, activation='tanh')(encoded_h2)encoded_h4 = Dense(8, activation='tanh')(encoded_h3)encoded_h5 = Dense(4, activation='tanh')(encoded_h4)latent = Dense(2, activation='tanh')(encoded_h5)decoder_h1 = Dense(4, activation='tanh')(latent)decoder_h2 = Dense(8, activation='tanh')(decoder_h1)decoder_h3 = Dense(16, activation='tanh')(decoder_h2)decoder_h4 = Dense(32, activation='tanh')(decoder_h3)decoder_h5 = Dense(64, activation='tanh')(decoder_h4)output = Dense(weights.shape[1], activation='tanh')(decoder_h5)autoencoder = Model(input_i,output)encoder = Model(input_i,latent)print autoencoder.summary()import keras.backend as Kimport tensorflow as tfdef embedded_mse(x_true, e_pred): print output print embedding mse = K.mean(K.square(output - embedding)) print mse return tf.Session().run(mse)autoencoder.compile(optimizer='adadelta', loss=embedded_mse)autoencoder.fit(X_train,X_train,epochs=10, batch_size=256, validation_split=.1)输出
(100000, 200)_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_47 (InputLayer) (None, 200) 0 _________________________________________________________________embedding_31 (Embedding) (None, 200, 100) 21833700 _________________________________________________________________dense_528 (Dense) (None, 200, 64) 6464 _________________________________________________________________dense_529 (Dense) (None, 200, 32) 2080 _________________________________________________________________dense_530 (Dense) (None, 200, 16) 528 _________________________________________________________________dense_531 (Dense) (None, 200, 8) 136 _________________________________________________________________dense_532 (Dense) (None, 200, 4) 36 _________________________________________________________________dense_533 (Dense) (None, 200, 2) 10 _________________________________________________________________dense_534 (Dense) (None, 200, 4) 12 _________________________________________________________________dense_535 (Dense) (None, 200, 8) 40 _________________________________________________________________dense_536 (Dense) (None, 200, 16) 144 _________________________________________________________________dense_537 (Dense) (None, 200, 32) 544 _________________________________________________________________dense_538 (Dense) (None, 200, 64) 2112 _________________________________________________________________dense_539 (Dense) (None, 200, 100) 6500 =================================================================Total params: 21,852,306Trainable params: 21,852,306Non-trainable params: 0_________________________________________________________________NoneTensor("dense_539/Tanh:0", shape=(?, 200, 100), dtype=float32)Tensor("embedding_31/Gather:0", shape=(?, 200, 100), dtype=float32)Tensor("loss_48/dense_539_loss/Mean:0", shape=(), dtype=float32)错误
---------------------------------------------------------------------------InvalidArgumentError Traceback (most recent call last)<ipython-input-155-a18e0c32f59b> in <module>() 1 autoencoder.compile(optimizer='adadelta',----> 2 loss=embedded_mse) 3 autoencoder.fit(X_train,embedding,epochs=10, 4 batch_size=256, validation_split=.1)/home/andrew/.local/lib/python2.7/site-packages/keras/engine/training.pyc in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs) 848 with K.name_scope(self.output_names[i] + '_loss'): 849 output_loss = weighted_loss(y_true, y_pred,--> 850 sample_weight, mask) 851 if len(self.outputs) > 1: 852 self.metrics_tensors.append(output_loss)/home/andrew/.local/lib/python2.7/site-packages/keras/engine/training.pyc in weighted(y_true, y_pred, weights, mask) 448 """ 449 # score_array has ndim >= 2--> 450 score_array = fn(y_true, y_pred) 451 if mask is not None: 452 # Cast the mask to floatX to avoid float64 upcasting in theano<ipython-input-153-73211fc383a5> in embedded_mse(x_true, e_pred) 7 print mse 8 ----> 9 return tf.Session().run(mse)/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata) 893 try: 894 result = self._run(None, fetches, feed_dict, options_ptr,--> 895 run_metadata_ptr) 896 if run_metadata: 897 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata) 1122 if final_fetches or final_targets or (handle and feed_dict_tensor): 1123 results = self._do_run(handle, final_targets, final_fetches,-> 1124 feed_dict_tensor, options, run_metadata) 1125 else: 1126 results = []/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 1319 if handle is None: 1320 return self._do_call(_run_fn, self._session, feeds, fetches, targets,-> 1321 options, run_metadata) 1322 else: 1323 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args) 1338 except KeyError: 1339 pass-> 1340 raise type(e)(node_def, op, message) 1341 1342 def _extend_graph(self):InvalidArgumentError: You must feed a value for placeholder tensor 'input_47' with dtype float and shape [?,200] [[Node: input_47 = Placeholder[dtype=DT_FLOAT, shape=[?,200], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]Caused by op u'input_47', defined at: File "/usr/lib/python2.7/runpy.py", line 174, in _run_module_as_main "__main__", fname, loader, pkg_name) File "/usr/lib/python2.7/runpy.py", line 72, in _run_code exec code in run_globals File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "/home/andrew/.local/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance app.start() File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 477, in start ioloop.IOLoop.instance().start() File "/home/andrew/.local/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start super(ZMQIOLoop, self).start() File "/home/andrew/.local/lib/python2.7/site-packages/tornado/ioloop.py", line 888, in start handler_func(fd_obj, events) File "/home/andrew/.local/lib/python2.7/site-packages/tornado/stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "/home/andrew/.local/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events self._handle_recv() File "/home/andrew/.local/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv self._run_callback(callback, msg) File "/home/andrew/.local/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback callback(*args, **kwargs) File "/home/andrew/.local/lib/python2.7/site-packages/tornado/stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell handler(stream, idents, msg) File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "/home/andrew/.local/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 533, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/home/andrew/.local/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2718, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "/home/andrew/.local/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes if self.run_code(code, result): File "/home/andrew/.local/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2882, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-152-7732fda181fc>", line 2, in <module> input_i = Input(shape=(200,)) File "/home/andrew/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 1436, in Input input_tensor=tensor) File "/home/andrew/.local/lib/python2.7/site-packages/keras/legacy/interfaces.py", line 87, in wrapper return func(*args, **kwargs) File "/home/andrew/.local/lib/python2.7/site-packages/keras/engine/topology.py", line 1347, in __init__ name=self.name) File "/home/andrew/.local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 442, in placeholder x = tf.placeholder(dtype, shape=shape, name=name) File "/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1548, in placeholder return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name) File "/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2094, in _placeholder name=name) File "/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op op_def=op_def) File "/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op original_op=self._default_original_op, op_def=op_def) File "/home/andrew/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-accessInvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_47' with dtype float and shape [?,200] [[Node: input_47 = Placeholder[dtype=DT_FLOAT, shape=[?,200], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]回答：
你的问题有一些问题（例如，weights是什么，在Embedding和最后的Dense层参数中使用了它？）。不过，我认为一个更简单的办法是将嵌入和自编码部分分开（它们是独立的），首先构建一个简单的嵌入模型，然后使用它的输出（通过predict）来馈送你的自编码器。这样你就不必定义自定义损失函数（顺便说一下，在这种函数中使用print语句不是一个好主意）。
在不知道你的数据细节的情况下，以下两个模型可以正常编译：
嵌入模型（从文档中快速改编）
model = Sequential()model.add(Embedding(1000, 64))model.compile('rmsprop', 'mse')自编码器：
input_i = Input(shape=(200,100))encoded_h1 = Dense(64, activation='tanh')(input_i)encoded_h2 = Dense(32, activation='tanh')(encoded_h1)encoded_h3 = Dense(16, activation='tanh')(encoded_h2)encoded_h4 = Dense(8, activation='tanh')(encoded_h3)encoded_h5 = Dense(4, activation='tanh')(encoded_h4)latent = Dense(2, activation='tanh')(encoded_h5)decoder_h1 = Dense(4, activation='tanh')(latent)decoder_h2 = Dense(8, activation='tanh')(decoder_h1)decoder_h3 = Dense(16, activation='tanh')(decoder_h2)decoder_h4 = Dense(32, activation='tanh')(decoder_h3)decoder_h5 = Dense(64, activation='tanh')(decoder_h4)output = Dense(100, activation='tanh')(decoder_h5)autoencoder = Model(input_i,output)autoencoder.compile('adadelta','mse')在将上述模型参数调整到你的情况后，这应该可以正常工作：
X_embedded = model.predict(X_train)autoencoder.fit(X_embedded,X_embedded,epochs=10, batch_size=256, validation_split=.1)